Biomedical Engineering Reference
In-Depth Information
as a change. Movement out of the design space is considered to be a change and would
normally initiate a regulatory postapproval change process. Design space is proposed
by the applicant and is subject to regulatory assessment and approval.” The primary
benefit of an approved design space is regulatory flexibility, most notably the
potential tomake process improvements within the design space with reduced regulatory
oversight [3, 4].
Process characterization studies are performed primarily at laboratory scale with
the purpose of defining the design space. Well-designed process characterization
studies can serve as a foundation for a successful process validation, regulatory
filing/approval, and subsequent manufacturing support over the life cycle of the
product [4, 5]. In the last decade, these studies have been widely used in the biotech
industry as a necessary precursor for setting performance acceptance criteria for
subsequent successful process validation at manufacturing scale. This is demonstrated
by the recent publications on related topics. The stepwise approach to characterization
of biotech processes consists of risk assessment, small-scale model (SSM) qualifica-
tion, process characterization studies, and setting process validation acceptance criteria
based on process characterization and historical process data [4, 5]. Failure modes and
effects analysis (FMEA) has been proposed as a tool that provides a rational approach
to evaluating a process and generating a ranked order of parameters requiring process
characterization [6].
Several publications have addressed the topics and provides guidelines for
performing small-scale modeling and qualification [4, 5]. Shukla et al. demonstrated
the utility of a design of experiments (DOE) approach to performing process charac-
terization of a metal-affinity chromatographic purification process for an Fc fusion
protein [8]. A fractional factorial studywasperformedtoexamine key operating
parameters and their interactions. The results of the DOE were subsequently used to
design the worst case studies to examine the robustness of the process. More recently,
publications have addressed the design and approach toward process characterization
of cell culture [9] and ion exchange unit operations [10]. Kaltenbrunner presented an
alternative screening method for identifying potential key operating parameters for an
ion exchange chromatography process. The advantages of this method based on
chromatographic theory over the commonly used fractional factorial screeningmethod
were discussed. Mollerup et al. [11] have recently published thermodynamic modeling
of chromatographic separation of proteins. Their approach aimed at supporting a more
in-depth understanding of the design and development that is necessary in the Quality
byDesign paradigm. Theywere able to successfully simulate the chromatographic step
and demonstrate the model's usefulness in identifying aberrations in step performance.
Finally, the statistical analysis approaches for setting process validation acceptance
criteria have been published recently [12]. Different statistical approaches including
mean
standard deviations, tolerance interval analysis, prediction profiler, and
Monte Carlo simulation were compared and benefits and disadvantages of these
methods discussed.
This chapter presents a stepwise approach to establishing the design space for a
biotech product in the form of a case study. It also discusses how the design space can
reduce process validation requirements and enhance regulatory flexibility.
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